There are many applications that utilize two or more microphones (e.g., microphone arrays) to pick up an acoustic signal. Separate microphone signals can be processed to obtain enhanced signals. One of the enhancement applications is acoustic beamforming, which means that sounds coming from different directions are attenuated differently. For example, if a person is speaking on the phone in a noisy environment, the acoustic beam can be directed towards the speaker, which will provide an improved signal-to-noise ratio of the picked signal, because the background noise is attenuated while the speech is preserved. For implementing acoustic beamforming successfully, matching microphone sensitivity is an important factor.
Majority, if not all, of the applications utilizing several microphones, benefit from matching the sensitivities of the microphones. If the frequency responses of the microphones are similar enough, only a full-band gain has to be applied into N−1 of the microphone signals if N is the number of microphones.
A conventional microphone capsule sensitivity tolerance is within a few decibels. This means that two random microphone capsules of the same type may have several decibels sensitivity difference. It is assumed that a sensitivity difference of a few decibels would be quite common in a product utilizing two or more microphones. On the other hand, the acoustic beamformer requires that the microphone sensitivities are matched more accurately; otherwise the beamformer may significantly deteriorate the desired signal.
A conventional way to match the microphone sensitivities is to use a manual calibration. This means that the individual microphone components are first measured using a suitable calibration measurement. After the measurement, matching microphone components are selected to be used in the array. Alternatively, the sensitivity differences found in the measurement can be compensated by building up a matched array. The compensation can be carried out either utilizing microphone specific full-band gains or, in case of non-similar frequency responses, microphone specific filters that match both the frequency responses and sensitivities of the microphones of the array. The manual method is obviously very expensive to be utilized in mass-production. Besides, possible later sensitivity mismatch due to the aging of the microphone components requires a new calibration.
Another group of calibration methods utilizes a dedicated signal source for calibrating the microphone array in place. This makes the re-calibration easier to carry out. The method usually requires an accurate knowledge about the placement of the microphones relative to the sound source. Also the calibration environment has to be controlled.
Yet another group of calibration methods is automatic self-calibration methods. These methods exploit the signals picked up by the microphones during normal operation of the array. For the calibration, typical implementations use either the whole signal or time intervals of the signal when the desired signal is active. When dealing with close-talking microphone arrays, the whole signal is not usable for the calibration purposes, since the sound pressure level of the desired signal is different at different microphones whereas the level of usual ambient noise is more or less the same at different microphones. Therefore, a separation between desired signal and ambient noise is required. If the desired signal is utilized for self-calibration, the microphone positions and the direction of arriving sound have to be known or estimated. Any estimation faults of these factors can cause errors in the calibration.